M. Islam

Artificial Intelligence · with Murase · 2003–2016

A researcher in the Science corpus: 3 papers; trusts 22 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 17 citations into it; #16,738 of 35,345 by trust here, #19,018 by citations
• Model Architectures — 14 citations into it
• Management Science and Operations Research — 12 citations into it; #7,596 of 11,946 by trust here, #10,392 by citations
• Optimization & Training — 6 citations into it
• Computer Vision and Pattern Recognition — 5 citations into it; #13,966 of 21,575 by trust here, #15,162 by citations
• Economics and Econometrics — 5 citations into it

Whom M. Islam trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Leo Breiman
2. Xin Yao
3. Yong Liu
4. Thomas G. Dietterich
5. David E. Rumelhart
6. Robert A. Jacobs
7. David B. Fogel
8. Thomas Bäck
9. Robert E. Schapire
10. James T. Kwok

Who trusts M. Islam overall

1. Shang He
2. Hong-gui Han
3. Lior Rokach
4. Kasun Bandara
5. Gavin Brown
6. Q. Henry Wu
7. J. Qiao
8. Christoph Bergmeir
9. Jeremy L. Wyatt
10. Corinna Cortes

Papers from M. Islam's perspective

Ordered by how strongly the researchers M. Islam trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
7. Attention is All you Need (Neural Information Processing Systems)
8. ImageNet Large Scale Visual Recognition Challenge
9. Learning Multiple Layers of Features from Tiny Images
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Layered Ensemble Architecture for Time Series Forecasting (2016) — 38 citations
• A New Adaptive Merging and Growing Algorithm for Designing Artificial Neural Networks (2009) — 129 citations
• A constructive algorithm for training cooperative neural network ensembles (2003) — 329 citations